Project_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "42cf65ea-d6e9-4691-8c15-df9afe744c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a4426cd8-074b-4703-b556-2c54a4feea20",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>87,057</td>\n",
       "      <td>695</td>\n",
       "      <td>60,804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>61,858</td>\n",
       "      <td>549</td>\n",
       "      <td>99,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>39,490</td>\n",
       "      <td>1,187</td>\n",
       "      <td>16,733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>81,431</td>\n",
       "      <td>1,917</td>\n",
       "      <td>6,392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>47,675</td>\n",
       "      <td>1,113</td>\n",
       "      <td>16,915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>41,655</td>\n",
       "      <td>1,145</td>\n",
       "      <td>6,238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category                           Sub-Category  \\\n",
       "0    Type of Household                         Married-couple   \n",
       "1    Type of Household  Female householder, no spouse present   \n",
       "2    Type of Household    Male householder, no spouse present   \n",
       "3    Type of Household                     Female householder   \n",
       "4    Type of Household                       Male householder   \n",
       "5   Race and Ethnicity                                  White   \n",
       "6   Race and Ethnicity                                  Black   \n",
       "7   Race and Ethnicity                                  Asian   \n",
       "8   Race and Ethnicity                    Hispanic (any race)   \n",
       "9   Age of Householder                         15 to 24 years   \n",
       "10  Age of Householder                         25 to 34 years   \n",
       "11  Age of Householder                         35 to 44 years   \n",
       "12  Age of Householder                         45 to 54 years   \n",
       "13  Age of Householder                         55 to 64 years   \n",
       "14  Age of Householder                     65 years and older   \n",
       "\n",
       "   Median Income Estimate (dollars) Margin of error (±) Number (thousands)  \n",
       "0                            87,057                 695             60,804  \n",
       "1                            41,027                 871             15,572  \n",
       "2                            58,051               2,172              6,452  \n",
       "3                            30,572                 603             22,858  \n",
       "4                            41,749                 701             20,539  \n",
       "5                            61,858                 549             99,400  \n",
       "6                            39,490               1,187             16,733  \n",
       "7                            81,431               1,917              6,392  \n",
       "8                            47,675               1,113             16,915  \n",
       "9                            41,655               1,145              6,238  \n",
       "10                           60,932                 802             20,109  \n",
       "11                           74,481               1,834             21,500  \n",
       "12                           77,213               1,156             22,808  \n",
       "13                           65,239               1,309             23,770  \n",
       "14                           39,823                 909             31,799  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check 2016 Incomce Data CSV to see if it reads properly & generate Data Frame\n",
    "csv_path1 = Path(\"2016_Income_Data.csv\")\n",
    "income1_df = pd.read_csv(csv_path1, encoding=\"UTF=8\")\n",
    "income1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "73cec3e7-ea60-46d6-8d2d-83aeabb90ae9",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
       "      <td>15,490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>71,231</td>\n",
       "      <td>736</td>\n",
       "      <td>101,582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>45,870</td>\n",
       "      <td>1,268</td>\n",
       "      <td>17,358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>94,903</td>\n",
       "      <td>3,794</td>\n",
       "      <td>6,987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>55,321</td>\n",
       "      <td>1,183</td>\n",
       "      <td>18,349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category                           Sub-Category  \\\n",
       "0    Type of Household                         Married-couple   \n",
       "1    Type of Household  Female householder, no spouse present   \n",
       "2    Type of Household    Male householder, no spouse present   \n",
       "3    Type of Household                     Female householder   \n",
       "4    Type of Household                       Male householder   \n",
       "5   Race and Ethnicity                                  White   \n",
       "6   Race and Ethnicity                                  Black   \n",
       "7   Race and Ethnicity                                  Asian   \n",
       "8   Race and Ethnicity                    Hispanic (any race)   \n",
       "9   Age of Householder                         15 to 24 years   \n",
       "10  Age of Householder                         25 to 34 years   \n",
       "11  Age of Householder                         35 to 44 years   \n",
       "12  Age of Householder                         45 to 54 years   \n",
       "13  Age of Householder                         55 to 64 years   \n",
       "14  Age of Householder                     65 years and older   \n",
       "\n",
       "   Median Income Estimate (dollars) Margin of error (±) Number (thousands)  \n",
       "0                           101,517                 850             61,454  \n",
       "1                            49,214               1,444             15,490  \n",
       "2                            67,304               2,317              6,963  \n",
       "3                            35,574                 685             24,244  \n",
       "4                            47,259               1,227             21,781  \n",
       "5                            71,231                 736            101,582  \n",
       "6                            45,870               1,268             17,358  \n",
       "7                            94,903               3,794              6,987  \n",
       "8                            55,321               1,183             18,349  \n",
       "9                            46,886               1,540              5,485  \n",
       "10                           71,566               1,154             20,654  \n",
       "11                           85,694               1,712             22,105  \n",
       "12                           90,359               1,958             21,663  \n",
       "13                           74,270               2,105             24,336  \n",
       "14                           46,360                 934             35,688  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check 2020 Incomce Data CSV to see if it reads properly & generate Data Frame\n",
    "csv_path2 = Path(\"2020_Income_Data.csv\")\n",
    "income2_df = pd.read_csv(csv_path2, encoding=\"UTF=8\")\n",
    "income2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f3b7df76-475f-490c-b170-6d5ccf2df01a",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>87,057</td>\n",
       "      <td>695</td>\n",
       "      <td>60,804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Median Income Estimate (dollars) Margin of error (±) Number (thousands)  \n",
       "0                           87,057                 695             60,804  \n",
       "1                           41,027                 871             15,572  \n",
       "2                           58,051               2,172              6,452  \n",
       "3                           30,572                 603             22,858  \n",
       "4                           41,749                 701             20,539  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# House Type Income DataFrame 2016\n",
    "housetype1_df = income1_df.iloc[0:5]\n",
    "housetype1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "b3966da0-4b52-4ab8-b77d-c4d959d85589",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
       "      <td>15,490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Median Income Estimate (dollars) Margin of error (±) Number (thousands)  \n",
       "0                          101,517                 850             61,454  \n",
       "1                           49,214               1,444             15,490  \n",
       "2                           67,304               2,317              6,963  \n",
       "3                           35,574                 685             24,244  \n",
       "4                           47,259               1,227             21,781  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# House Type Income DataFrame 2020\n",
    "housetype2_df = income2_df.iloc[0:5]\n",
    "housetype2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "6fb0518f-edda-4165-8be0-d265f2fd65b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category_x</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
       "      <th>Number (thousands)_x</th>\n",
       "      <th>Category_y</th>\n",
       "      <th>Median Income Estimate (dollars)_y</th>\n",
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       "      <th>Number (thousands)_y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>87,057</td>\n",
       "      <td>695</td>\n",
       "      <td>60,804</td>\n",
       "      <td>Type of Household</td>\n",
       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "      <td>Type of Household</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
       "      <td>15,490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "      <td>Type of Household</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "      <td>Type of Household</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "      <td>Type of Household</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category_x                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Median Income Estimate (dollars)_x Margin of error (±)_x  \\\n",
       "0                             87,057                   695   \n",
       "1                             41,027                   871   \n",
       "2                             58,051                 2,172   \n",
       "3                             30,572                   603   \n",
       "4                             41,749                   701   \n",
       "\n",
       "  Number (thousands)_x         Category_y Median Income Estimate (dollars)_y  \\\n",
       "0               60,804  Type of Household                            101,517   \n",
       "1               15,572  Type of Household                             49,214   \n",
       "2                6,452  Type of Household                             67,304   \n",
       "3               22,858  Type of Household                             35,574   \n",
       "4               20,539  Type of Household                             47,259   \n",
       "\n",
       "  Margin of error (±)_y Number (thousands)_y  \n",
       "0                   850               61,454  \n",
       "1                 1,444               15,490  \n",
       "2                 2,317                6,963  \n",
       "3                   685               24,244  \n",
       "4                 1,227               21,781  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge House Type DataFrame\n",
    "housetype_merge = pd.merge(housetype1_df, housetype2_df, how=\"left\", on=\"Sub-Category\")\n",
    "housetype_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "72443bd7-3eac-417d-9277-7502075b8a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category_x</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
       "      <th>Number (thousands)_x</th>\n",
       "      <th>Median Income Estimate (dollars)_y</th>\n",
       "      <th>Margin of error (±)_y</th>\n",
       "      <th>Number (thousands)_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>87,057</td>\n",
       "      <td>695</td>\n",
       "      <td>60,804</td>\n",
       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category_x                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Median Income Estimate (dollars)_x Margin of error (±)_x  \\\n",
       "0                             87,057                   695   \n",
       "1                             41,027                   871   \n",
       "2                             58,051                 2,172   \n",
       "3                             30,572                   603   \n",
       "4                             41,749                   701   \n",
       "\n",
       "  Number (thousands)_x Median Income Estimate (dollars)_y  \\\n",
       "0               60,804                            101,517   \n",
       "1               15,572                             49,214   \n",
       "2                6,452                             67,304   \n",
       "3               22,858                             35,574   \n",
       "4               20,539                             47,259   \n",
       "\n",
       "  Margin of error (±)_y Number (thousands)_y  \n",
       "0                   850               61,454  \n",
       "1                 1,444               15,490  \n",
       "2                 2,317                6,963  \n",
       "3                   685               24,244  \n",
       "4                 1,227               21,781  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_y\"\n",
    "housetype_merge = housetype_merge.drop(\"Category_y\", axis=1)\n",
    "housetype_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "ce403574-5330-4666-9f62-9724e91d594c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
       "      <th>Number (thousands)_x</th>\n",
       "      <th>Median Income Estimate (dollars)_y</th>\n",
       "      <th>Margin of error (±)_y</th>\n",
       "      <th>Number (thousands)_y</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Married-couple</td>\n",
       "      <td>87,057</td>\n",
       "      <td>695</td>\n",
       "      <td>60,804</td>\n",
       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
       "      <td>15,490</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0                         Married-couple                             87,057   \n",
       "1  Female householder, no spouse present                             41,027   \n",
       "2    Male householder, no spouse present                             58,051   \n",
       "3                     Female householder                             30,572   \n",
       "4                       Male householder                             41,749   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x  \\\n",
       "0                   695               60,804   \n",
       "1                   871               15,572   \n",
       "2                 2,172                6,452   \n",
       "3                   603               22,858   \n",
       "4                   701               20,539   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                            101,517                   850   \n",
       "1                             49,214                 1,444   \n",
       "2                             67,304                 2,317   \n",
       "3                             35,574                   685   \n",
       "4                             47,259                 1,227   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0               61,454  \n",
       "1               15,490  \n",
       "2                6,963  \n",
       "3               24,244  \n",
       "4               21,781  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_x\"\n",
    "housetype_merge = housetype_merge.drop(\"Category_x\", axis=1)\n",
    "housetype_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "4d40380d-fa9f-4707-bf9c-2f1479a139ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Household Type</th>\n",
       "      <th>Median Income Estimate (dollars) 2016</th>\n",
       "      <th>Margin of error (±) 2016</th>\n",
       "      <th>Number (thousands) 2016</th>\n",
       "      <th>Median Income Estimate (dollars) 2020</th>\n",
       "      <th>Margin of error (±) 2020</th>\n",
       "      <th>Number (thousands) 2020</th>\n",
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       "      <th>0</th>\n",
       "      <td>Married-couple</td>\n",
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       "      <td>695</td>\n",
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       "      <td>101,517</td>\n",
       "      <td>850</td>\n",
       "      <td>61,454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>41,027</td>\n",
       "      <td>871</td>\n",
       "      <td>15,572</td>\n",
       "      <td>49,214</td>\n",
       "      <td>1,444</td>\n",
       "      <td>15,490</td>\n",
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       "      <th>2</th>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>58,051</td>\n",
       "      <td>2,172</td>\n",
       "      <td>6,452</td>\n",
       "      <td>67,304</td>\n",
       "      <td>2,317</td>\n",
       "      <td>6,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,572</td>\n",
       "      <td>603</td>\n",
       "      <td>22,858</td>\n",
       "      <td>35,574</td>\n",
       "      <td>685</td>\n",
       "      <td>24,244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male householder</td>\n",
       "      <td>41,749</td>\n",
       "      <td>701</td>\n",
       "      <td>20,539</td>\n",
       "      <td>47,259</td>\n",
       "      <td>1,227</td>\n",
       "      <td>21,781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Household Type  \\\n",
       "0                         Married-couple   \n",
       "1  Female householder, no spouse present   \n",
       "2    Male householder, no spouse present   \n",
       "3                     Female householder   \n",
       "4                       Male householder   \n",
       "\n",
       "  Median Income Estimate (dollars) 2016 Margin of error (±) 2016  \\\n",
       "0                                87,057                      695   \n",
       "1                                41,027                      871   \n",
       "2                                58,051                    2,172   \n",
       "3                                30,572                      603   \n",
       "4                                41,749                      701   \n",
       "\n",
       "  Number (thousands) 2016 Median Income Estimate (dollars) 2020  \\\n",
       "0                  60,804                               101,517   \n",
       "1                  15,572                                49,214   \n",
       "2                   6,452                                67,304   \n",
       "3                  22,858                                35,574   \n",
       "4                  20,539                                47,259   \n",
       "\n",
       "  Margin of error (±) 2020 Number (thousands) 2020  \n",
       "0                      850                  61,454  \n",
       "1                    1,444                  15,490  \n",
       "2                    2,317                   6,963  \n",
       "3                      685                  24,244  \n",
       "4                    1,227                  21,781  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change and rename category names\n",
    "newhousehold_df = housetype_merge.rename(columns={\"Median Income Estimate (dollars)_x\": \"Median Income Estimate (dollars) 2016\", \n",
    "                                         \"Margin of error (±)_x\": \"Margin of error (±) 2016\",\n",
    "                                         \"Number (thousands)_x\": \"Number (thousands) 2016\",\n",
    "                                         \"Median Income Estimate (dollars)_y\": \"Median Income Estimate (dollars) 2020\",\n",
    "                                         \"Margin of error (±)_y\": \"Margin of error (±) 2020\",\n",
    "                                         \"Number (thousands)_y\": \"Number (thousands) 2020\",\n",
    "                                         \"Sub-Category\": \"Household Type\"\n",
    "                                        })\n",
    "newhousehold_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "efaabcac-60d1-4f56-bbdf-8cdcde44fb3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate bar plot showing 2016 vs 2020 income differnce\n",
    "household_type = (\"Married-Couple\", \"Female householder, no spouse present\", \"Male householder, no spouse present\", \"Female householder\", \"Male householder\")\n",
    "median_income = {\n",
    "    'Median Income Estimate (dollars) 2016': (87057, 41027, 58051, 30572, 41749),\n",
    "    'Median Income Estimate (dollars) 2020': (101517, 49214, 67304, 35574, 47259),\n",
    "}\n",
    "\n",
    "x = np.arange(len(household_type))\n",
    "width = 0.25\n",
    "multiplier = 0\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for attribute, measurement in median_income.items():\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, measurement, width, label=attribute)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Median Income Estimate (dollars)')\n",
    "ax.set_title('2016 & 2020 Median Income vs Household Type')\n",
    "ax.set_xticks(x + width / 2)\n",
    "ax.set_xticklabels(household_type)\n",
    "ax.legend(loc='upper left', ncols=3)\n",
    "ax.set_ylim(0, 110000)\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('HouseholdIncome.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "9437165e-aa3c-4dda-8c34-10c5757e638a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>61,858</td>\n",
       "      <td>549</td>\n",
       "      <td>99,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>39,490</td>\n",
       "      <td>1,187</td>\n",
       "      <td>16,733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>81,431</td>\n",
       "      <td>1,917</td>\n",
       "      <td>6,392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>47,675</td>\n",
       "      <td>1,113</td>\n",
       "      <td>16,915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category         Sub-Category Median Income Estimate (dollars)  \\\n",
       "5  Race and Ethnicity                White                           61,858   \n",
       "6  Race and Ethnicity                Black                           39,490   \n",
       "7  Race and Ethnicity                Asian                           81,431   \n",
       "8  Race and Ethnicity  Hispanic (any race)                           47,675   \n",
       "\n",
       "  Margin of error (±) Number (thousands)  \n",
       "5                 549             99,400  \n",
       "6               1,187             16,733  \n",
       "7               1,917              6,392  \n",
       "8               1,113             16,915  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ethnicity Income DataFrame 2016\n",
    "ethnicity1_df = income1_df.iloc[5:9]\n",
    "ethnicity1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "4cf0e0f4-5d50-4e07-a6a0-8bbe1fc3d763",
   "metadata": {},
   "outputs": [
    {
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       "      <td>71,231</td>\n",
       "      <td>736</td>\n",
       "      <td>101,582</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>45,870</td>\n",
       "      <td>1,268</td>\n",
       "      <td>17,358</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
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       "      <td>6,987</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Hispanic (any race)</td>\n",
       "      <td>55,321</td>\n",
       "      <td>1,183</td>\n",
       "      <td>18,349</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             Category         Sub-Category Median Income Estimate (dollars)  \\\n",
       "5  Race and Ethnicity                White                           71,231   \n",
       "6  Race and Ethnicity                Black                           45,870   \n",
       "7  Race and Ethnicity                Asian                           94,903   \n",
       "8  Race and Ethnicity  Hispanic (any race)                           55,321   \n",
       "\n",
       "  Margin of error (±) Number (thousands)  \n",
       "5                 736            101,582  \n",
       "6               1,268             17,358  \n",
       "7               3,794              6,987  \n",
       "8               1,183             18,349  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ethnicity Income DataFrame 2020\n",
    "ethnicity2_df = income2_df.iloc[5:9]\n",
    "ethnicity2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "486d4db3-ebe3-49e2-ad54-57e14b834481",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>61,858</td>\n",
       "      <td>549</td>\n",
       "      <td>99,400</td>\n",
       "      <td>Race and Ethnicity</td>\n",
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       "      <td>736</td>\n",
       "      <td>101,582</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>39,490</td>\n",
       "      <td>1,187</td>\n",
       "      <td>16,733</td>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>45,870</td>\n",
       "      <td>1,268</td>\n",
       "      <td>17,358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>81,431</td>\n",
       "      <td>1,917</td>\n",
       "      <td>6,392</td>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>94,903</td>\n",
       "      <td>3,794</td>\n",
       "      <td>6,987</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>47,675</td>\n",
       "      <td>1,113</td>\n",
       "      <td>16,915</td>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>55,321</td>\n",
       "      <td>1,183</td>\n",
       "      <td>18,349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category_x         Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0  Race and Ethnicity                White                             61,858   \n",
       "1  Race and Ethnicity                Black                             39,490   \n",
       "2  Race and Ethnicity                Asian                             81,431   \n",
       "3  Race and Ethnicity  Hispanic (any race)                             47,675   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x          Category_y  \\\n",
       "0                   549               99,400  Race and Ethnicity   \n",
       "1                 1,187               16,733  Race and Ethnicity   \n",
       "2                 1,917                6,392  Race and Ethnicity   \n",
       "3                 1,113               16,915  Race and Ethnicity   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             71,231                   736   \n",
       "1                             45,870                 1,268   \n",
       "2                             94,903                 3,794   \n",
       "3                             55,321                 1,183   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0              101,582  \n",
       "1               17,358  \n",
       "2                6,987  \n",
       "3               18,349  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Ethnicity DataFrames\n",
    "ethnicity_merge = pd.merge(ethnicity1_df, ethnicity2_df, how=\"left\", on=\"Sub-Category\")\n",
    "ethnicity_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "8a07231b-e03f-4d41-abdf-c34f573fe193",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Race and Ethnicity</td>\n",
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       "      <td>61,858</td>\n",
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       "      <td>99,400</td>\n",
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       "      <td>736</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>39,490</td>\n",
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       "      <td>81,431</td>\n",
       "      <td>1,917</td>\n",
       "      <td>6,392</td>\n",
       "      <td>94,903</td>\n",
       "      <td>3,794</td>\n",
       "      <td>6,987</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>47,675</td>\n",
       "      <td>1,113</td>\n",
       "      <td>16,915</td>\n",
       "      <td>55,321</td>\n",
       "      <td>1,183</td>\n",
       "      <td>18,349</td>\n",
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      "text/plain": [
       "           Category_x         Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0  Race and Ethnicity                White                             61,858   \n",
       "1  Race and Ethnicity                Black                             39,490   \n",
       "2  Race and Ethnicity                Asian                             81,431   \n",
       "3  Race and Ethnicity  Hispanic (any race)                             47,675   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x  \\\n",
       "0                   549               99,400   \n",
       "1                 1,187               16,733   \n",
       "2                 1,917                6,392   \n",
       "3                 1,113               16,915   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             71,231                   736   \n",
       "1                             45,870                 1,268   \n",
       "2                             94,903                 3,794   \n",
       "3                             55,321                 1,183   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0              101,582  \n",
       "1               17,358  \n",
       "2                6,987  \n",
       "3               18,349  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_y\"\n",
    "ethnicity_merge = ethnicity_merge.drop(\"Category_y\", axis=1)\n",
    "ethnicity_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "6546292e-4684-48f7-9330-0c0db8af1b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>94,903</td>\n",
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       "      <td>6,987</td>\n",
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      "text/plain": [
       "          Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0                White                             61,858   \n",
       "1                Black                             39,490   \n",
       "2                Asian                             81,431   \n",
       "3  Hispanic (any race)                             47,675   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x  \\\n",
       "0                   549               99,400   \n",
       "1                 1,187               16,733   \n",
       "2                 1,917                6,392   \n",
       "3                 1,113               16,915   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             71,231                   736   \n",
       "1                             45,870                 1,268   \n",
       "2                             94,903                 3,794   \n",
       "3                             55,321                 1,183   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0              101,582  \n",
       "1               17,358  \n",
       "2                6,987  \n",
       "3               18,349  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_x\"\n",
    "ethnicity_merge = ethnicity_merge.drop(\"Category_x\", axis=1)\n",
    "ethnicity_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "524d9336-b0cd-4703-8740-36cbf0474319",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1,917</td>\n",
       "      <td>6,392</td>\n",
       "      <td>94,903</td>\n",
       "      <td>3,794</td>\n",
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      "text/plain": [
       "             Ethnicity Median Income Estimate (dollars) 2016  \\\n",
       "0                White                                61,858   \n",
       "1                Black                                39,490   \n",
       "2                Asian                                81,431   \n",
       "3  Hispanic (any race)                                47,675   \n",
       "\n",
       "  Margin of error (±) 2016 Number (thousands) 2016  \\\n",
       "0                      549                  99,400   \n",
       "1                    1,187                  16,733   \n",
       "2                    1,917                   6,392   \n",
       "3                    1,113                  16,915   \n",
       "\n",
       "  Median Income Estimate (dollars) 2020 Margin of error (±) 2020  \\\n",
       "0                                71,231                      736   \n",
       "1                                45,870                    1,268   \n",
       "2                                94,903                    3,794   \n",
       "3                                55,321                    1,183   \n",
       "\n",
       "  Number (thousands) 2020  \n",
       "0                 101,582  \n",
       "1                  17,358  \n",
       "2                   6,987  \n",
       "3                  18,349  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change Ethnicity rename category names\n",
    "newethnicity_df = ethnicity_merge.rename(columns={\"Median Income Estimate (dollars)_x\": \"Median Income Estimate (dollars) 2016\", \n",
    "                                         \"Margin of error (±)_x\": \"Margin of error (±) 2016\",\n",
    "                                         \"Number (thousands)_x\": \"Number (thousands) 2016\",\n",
    "                                         \"Median Income Estimate (dollars)_y\": \"Median Income Estimate (dollars) 2020\",\n",
    "                                         \"Margin of error (±)_y\": \"Margin of error (±) 2020\",\n",
    "                                         \"Number (thousands)_y\": \"Number (thousands) 2020\",\n",
    "                                         \"Sub-Category\": \"Ethnicity\"\n",
    "                                        })\n",
    "newethnicity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "01f1a889-b214-430c-8dff-742db13a7549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate bar plot showing 2016 vs 2020 income differnce\n",
    "ethnicity = (\"White\", \"Black\", \"Asian\", \"Hispanic (any race)\")\n",
    "median_income = {\n",
    "    'Median Income Estimate (dollars) 2016': (61858, 39490, 81431, 47675),\n",
    "    'Median Income Estimate (dollars) 2020': (71231, 45870, 94903, 55321),\n",
    "}\n",
    "\n",
    "x = np.arange(len(ethnicity))\n",
    "width = 0.25\n",
    "multiplier = 0\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for attribute, measurement in median_income.items():\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, measurement, width, label=attribute)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Median Income Estimate (dollars)')\n",
    "ax.set_title('2016 & 2020 Median Income vs Ethnicity')\n",
    "ax.set_xticks(x + width / 2)\n",
    "ax.set_xticklabels(ethnicity)\n",
    "ax.legend(loc='upper left', ncols=4)\n",
    "ax.set_ylim(0, 100000)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('EthnicityIncome.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2f45d8ff-dba2-449f-b90d-cb671ac8082b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>41,655</td>\n",
       "      <td>1,145</td>\n",
       "      <td>6,238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category        Sub-Category Median Income Estimate (dollars)  \\\n",
       "9   Age of Householder      15 to 24 years                           41,655   \n",
       "10  Age of Householder      25 to 34 years                           60,932   \n",
       "11  Age of Householder      35 to 44 years                           74,481   \n",
       "12  Age of Householder      45 to 54 years                           77,213   \n",
       "13  Age of Householder      55 to 64 years                           65,239   \n",
       "14  Age of Householder  65 years and older                           39,823   \n",
       "\n",
       "   Margin of error (±) Number (thousands)  \n",
       "9                1,145              6,238  \n",
       "10                 802             20,109  \n",
       "11               1,834             21,500  \n",
       "12               1,156             22,808  \n",
       "13               1,309             23,770  \n",
       "14                 909             31,799  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age Income DataFrame 2016\n",
    "age1_df = income1_df.iloc[9:15]\n",
    "age1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "67f594d9-28ad-44fd-957f-fa925dcf99b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)</th>\n",
       "      <th>Margin of error (±)</th>\n",
       "      <th>Number (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category        Sub-Category Median Income Estimate (dollars)  \\\n",
       "9   Age of Householder      15 to 24 years                           46,886   \n",
       "10  Age of Householder      25 to 34 years                           71,566   \n",
       "11  Age of Householder      35 to 44 years                           85,694   \n",
       "12  Age of Householder      45 to 54 years                           90,359   \n",
       "13  Age of Householder      55 to 64 years                           74,270   \n",
       "14  Age of Householder  65 years and older                           46,360   \n",
       "\n",
       "   Margin of error (±) Number (thousands)  \n",
       "9                1,540              5,485  \n",
       "10               1,154             20,654  \n",
       "11               1,712             22,105  \n",
       "12               1,958             21,663  \n",
       "13               2,105             24,336  \n",
       "14                 934             35,688  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age Income DataFrame 2020\n",
    "age2_df = income2_df.iloc[9:15]\n",
    "age2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "a14b2106-cdb2-4e80-a6c9-72f97ad3a24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category_x</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>41,655</td>\n",
       "      <td>1,145</td>\n",
       "      <td>6,238</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category_x        Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0  Age of Householder      15 to 24 years                             41,655   \n",
       "1  Age of Householder      25 to 34 years                             60,932   \n",
       "2  Age of Householder      35 to 44 years                             74,481   \n",
       "3  Age of Householder      45 to 54 years                             77,213   \n",
       "4  Age of Householder      55 to 64 years                             65,239   \n",
       "5  Age of Householder  65 years and older                             39,823   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x          Category_y  \\\n",
       "0                 1,145                6,238  Age of Householder   \n",
       "1                   802               20,109  Age of Householder   \n",
       "2                 1,834               21,500  Age of Householder   \n",
       "3                 1,156               22,808  Age of Householder   \n",
       "4                 1,309               23,770  Age of Householder   \n",
       "5                   909               31,799  Age of Householder   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             46,886                 1,540   \n",
       "1                             71,566                 1,154   \n",
       "2                             85,694                 1,712   \n",
       "3                             90,359                 1,958   \n",
       "4                             74,270                 2,105   \n",
       "5                             46,360                   934   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0                5,485  \n",
       "1               20,654  \n",
       "2               22,105  \n",
       "3               21,663  \n",
       "4               24,336  \n",
       "5               35,688  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Age DataFrame\n",
    "age_merge = pd.merge(age1_df, age2_df, how=\"left\", on=\"Sub-Category\")\n",
    "age_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "058fdbb4-a610-43ff-8f28-e5861a262416",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category_x</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
       "      <th>Number (thousands)_x</th>\n",
       "      <th>Median Income Estimate (dollars)_y</th>\n",
       "      <th>Margin of error (±)_y</th>\n",
       "      <th>Number (thousands)_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>41,655</td>\n",
       "      <td>1,145</td>\n",
       "      <td>6,238</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category_x        Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0  Age of Householder      15 to 24 years                             41,655   \n",
       "1  Age of Householder      25 to 34 years                             60,932   \n",
       "2  Age of Householder      35 to 44 years                             74,481   \n",
       "3  Age of Householder      45 to 54 years                             77,213   \n",
       "4  Age of Householder      55 to 64 years                             65,239   \n",
       "5  Age of Householder  65 years and older                             39,823   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x  \\\n",
       "0                 1,145                6,238   \n",
       "1                   802               20,109   \n",
       "2                 1,834               21,500   \n",
       "3                 1,156               22,808   \n",
       "4                 1,309               23,770   \n",
       "5                   909               31,799   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             46,886                 1,540   \n",
       "1                             71,566                 1,154   \n",
       "2                             85,694                 1,712   \n",
       "3                             90,359                 1,958   \n",
       "4                             74,270                 2,105   \n",
       "5                             46,360                   934   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0                5,485  \n",
       "1               20,654  \n",
       "2               22,105  \n",
       "3               21,663  \n",
       "4               24,336  \n",
       "5               35,688  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_y\"\n",
    "age_merge = age_merge.drop(\"Category_y\", axis=1)\n",
    "age_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "36feced5-b8d7-40cf-8351-150c0bcf2039",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Median Income Estimate (dollars)_x</th>\n",
       "      <th>Margin of error (±)_x</th>\n",
       "      <th>Number (thousands)_x</th>\n",
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       "      <th>0</th>\n",
       "      <td>15 to 24 years</td>\n",
       "      <td>41,655</td>\n",
       "      <td>1,145</td>\n",
       "      <td>6,238</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
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       "      <th>2</th>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sub-Category Median Income Estimate (dollars)_x  \\\n",
       "0      15 to 24 years                             41,655   \n",
       "1      25 to 34 years                             60,932   \n",
       "2      35 to 44 years                             74,481   \n",
       "3      45 to 54 years                             77,213   \n",
       "4      55 to 64 years                             65,239   \n",
       "5  65 years and older                             39,823   \n",
       "\n",
       "  Margin of error (±)_x Number (thousands)_x  \\\n",
       "0                 1,145                6,238   \n",
       "1                   802               20,109   \n",
       "2                 1,834               21,500   \n",
       "3                 1,156               22,808   \n",
       "4                 1,309               23,770   \n",
       "5                   909               31,799   \n",
       "\n",
       "  Median Income Estimate (dollars)_y Margin of error (±)_y  \\\n",
       "0                             46,886                 1,540   \n",
       "1                             71,566                 1,154   \n",
       "2                             85,694                 1,712   \n",
       "3                             90,359                 1,958   \n",
       "4                             74,270                 2,105   \n",
       "5                             46,360                   934   \n",
       "\n",
       "  Number (thousands)_y  \n",
       "0                5,485  \n",
       "1               20,654  \n",
       "2               22,105  \n",
       "3               21,663  \n",
       "4               24,336  \n",
       "5               35,688  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Column \"Category_x\"\n",
    "age_merge = age_merge.drop(\"Category_x\", axis=1)\n",
    "age_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "bd5923b9-c72c-4b04-9e5b-ed33319dc486",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Age</th>\n",
       "      <th>Median Income Estimate (dollars) 2016</th>\n",
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       "      <th>Number (thousands) 2020</th>\n",
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       "      <th>0</th>\n",
       "      <td>15 to 24 years</td>\n",
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       "      <td>6,238</td>\n",
       "      <td>46,886</td>\n",
       "      <td>1,540</td>\n",
       "      <td>5,485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>60,932</td>\n",
       "      <td>802</td>\n",
       "      <td>20,109</td>\n",
       "      <td>71,566</td>\n",
       "      <td>1,154</td>\n",
       "      <td>20,654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>74,481</td>\n",
       "      <td>1,834</td>\n",
       "      <td>21,500</td>\n",
       "      <td>85,694</td>\n",
       "      <td>1,712</td>\n",
       "      <td>22,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>77,213</td>\n",
       "      <td>1,156</td>\n",
       "      <td>22,808</td>\n",
       "      <td>90,359</td>\n",
       "      <td>1,958</td>\n",
       "      <td>21,663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>65,239</td>\n",
       "      <td>1,309</td>\n",
       "      <td>23,770</td>\n",
       "      <td>74,270</td>\n",
       "      <td>2,105</td>\n",
       "      <td>24,336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>65 years and older</td>\n",
       "      <td>39,823</td>\n",
       "      <td>909</td>\n",
       "      <td>31,799</td>\n",
       "      <td>46,360</td>\n",
       "      <td>934</td>\n",
       "      <td>35,688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age Median Income Estimate (dollars) 2016  \\\n",
       "0      15 to 24 years                                41,655   \n",
       "1      25 to 34 years                                60,932   \n",
       "2      35 to 44 years                                74,481   \n",
       "3      45 to 54 years                                77,213   \n",
       "4      55 to 64 years                                65,239   \n",
       "5  65 years and older                                39,823   \n",
       "\n",
       "  Margin of error (±) 2016 Number (thousands) 2016  \\\n",
       "0                    1,145                   6,238   \n",
       "1                      802                  20,109   \n",
       "2                    1,834                  21,500   \n",
       "3                    1,156                  22,808   \n",
       "4                    1,309                  23,770   \n",
       "5                      909                  31,799   \n",
       "\n",
       "  Median Income Estimate (dollars) 2020 Margin of error (±) 2020  \\\n",
       "0                                46,886                    1,540   \n",
       "1                                71,566                    1,154   \n",
       "2                                85,694                    1,712   \n",
       "3                                90,359                    1,958   \n",
       "4                                74,270                    2,105   \n",
       "5                                46,360                      934   \n",
       "\n",
       "  Number (thousands) 2020  \n",
       "0                   5,485  \n",
       "1                  20,654  \n",
       "2                  22,105  \n",
       "3                  21,663  \n",
       "4                  24,336  \n",
       "5                  35,688  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change Age rename category names\n",
    "newage_df = age_merge.rename(columns={\"Median Income Estimate (dollars)_x\": \"Median Income Estimate (dollars) 2016\", \n",
    "                                         \"Margin of error (±)_x\": \"Margin of error (±) 2016\",\n",
    "                                         \"Number (thousands)_x\": \"Number (thousands) 2016\",\n",
    "                                         \"Median Income Estimate (dollars)_y\": \"Median Income Estimate (dollars) 2020\",\n",
    "                                         \"Margin of error (±)_y\": \"Margin of error (±) 2020\",\n",
    "                                         \"Number (thousands)_y\": \"Number (thousands) 2020\",\n",
    "                                         \"Sub-Category\": \"Age\"\n",
    "                                        })\n",
    "newage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "4479cb58-ef84-465a-a2e3-d6103c9ed06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate bar plot showing 2016 vs 2020 income differnce in Age\n",
    "age_ranges = (\"15 to 24 years\", \"25 to 34 years\", \"35 to 44 years\", \"45 to 54 years\", \"55 to 64 years\", \"65 years and older\")\n",
    "median_income = {\n",
    "    'Median Income Estimate (dollars) 2016': (41655, 60932, 74481, 77213, 65239, 39823),\n",
    "    'Median Income Estimate (dollars) 2020': (46886, 71566, 85694, 90359, 74270, 46360),\n",
    "}\n",
    "\n",
    "x = np.arange(len(age_ranges))\n",
    "width = 0.25\n",
    "multiplier = 0\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "\n",
    "for attribute, measurement in median_income.items():\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, measurement, width, label=attribute)\n",
    "    ax.bar_label(rects, padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Median Income Estimate (dollars)')\n",
    "ax.set_title('2016 & 2020 Median Income vs Age')\n",
    "ax.set_xticks(x + width / 2)\n",
    "ax.set_xticklabels(age_ranges)\n",
    "ax.legend(loc='upper left', ncols=4)\n",
    "ax.set_ylim(0, 100000)\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('AgeIncome.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ff75bf-5a65-40f8-a174-82443281482a",
   "metadata": {},
   "source": [
    "Voter Turnout and Voter Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "89f1ec43-814d-4634-9507-df11e3b9dbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Voting-Eligible Population (VEP)</th>\n",
       "      <th>Total Ballots Counted (Estimate)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230,931,921</td>\n",
       "      <td>138,846,571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Voting-Eligible Population (VEP) Total Ballots Counted (Estimate)\n",
       "0                      230,931,921                      138,846,571"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate CSV Data Frame for National Election Voting Turnout 2016\n",
    "csv_path3 = Path(\"2016_Voter_Data.csv\")\n",
    "voter1_df = pd.read_csv(csv_path3, encoding=\"UTF=8\")\n",
    "voter1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "b4771f93-9a6d-41e9-8554-e0528e4bc957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate pie chart showing Votes Cast vs Uncast Votes for 2016\n",
    "labels = [\"Uncasted Votes\", \"Total Ballots Counted (Estimate)\"]\n",
    "votes = [(230931921-138846571), 138846571]\n",
    "colors = [\"red\",\"green\"]\n",
    "explode = (0.1,0.1)\n",
    "plt.pie(votes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=140)\n",
    "plt.title(\"Voter Turnout Percentage 2016\")\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('VoteTurnout2016.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "8f194566-a345-41f1-832e-3924366e1ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Voting-Eligible Population (VEP)</th>\n",
       "      <th>Total Ballots Counted (Estimate)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>239,924,038</td>\n",
       "      <td>159,738,337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Voting-Eligible Population (VEP) Total Ballots Counted (Estimate)\n",
       "0                      239,924,038                      159,738,337"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate CSV Data Frame for National Election Voting Turnout 2020\n",
    "csv_path4 = Path(\"2020_Voter_Data.csv\")\n",
    "voter2_df = pd.read_csv(csv_path4, encoding=\"UTF=8\")\n",
    "voter2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "83b5be72-0795-4337-96d9-85ce0bb5b7b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate pie chart showing Votes Cast vs Uncast Votes for 2020\n",
    "labels = [\"Uncasted Votes\", \"Total Ballots Counted (Estimate)\"]\n",
    "votes = [(239924038-159738337), 159738337]\n",
    "colors = [\"red\",\"green\"]\n",
    "explode = (0.1,0.1)\n",
    "plt.pie(votes, explode=explode, labels=labels, colors=colors,\n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=140)\n",
    "plt.title(\"Voter Turnout Percentage 2020\")\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('VoterTurnout2020.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9265e124-96d0-4a1d-82da-dd207e6289ff",
   "metadata": {},
   "source": [
    "Calculate Voter Data Per Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "60e85973-4479-4858-8707-587b07042210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>114,350</td>\n",
       "      <td>99,595</td>\n",
       "      <td>79,382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>1,503</td>\n",
       "      <td>1,230</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>1,359</td>\n",
       "      <td>1,078</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,089</td>\n",
       "      <td>24,561</td>\n",
       "      <td>16,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>33,101</td>\n",
       "      <td>26,603</td>\n",
       "      <td>14,855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>177,865</td>\n",
       "      <td>152,285</td>\n",
       "      <td>111,891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>28,808</td>\n",
       "      <td>23,716</td>\n",
       "      <td>17,119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>10,283</td>\n",
       "      <td>8,252</td>\n",
       "      <td>5,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>26,662</td>\n",
       "      <td>21,661</td>\n",
       "      <td>12,682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>18 to 24 years</td>\n",
       "      <td>26,913</td>\n",
       "      <td>21,555</td>\n",
       "      <td>11,560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>38,283</td>\n",
       "      <td>31,868</td>\n",
       "      <td>20,332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>34,327</td>\n",
       "      <td>29,228</td>\n",
       "      <td>20,662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>38,301</td>\n",
       "      <td>32,683</td>\n",
       "      <td>25,012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>39,242</td>\n",
       "      <td>33,774</td>\n",
       "      <td>26,657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>46,993</td>\n",
       "      <td>41,110</td>\n",
       "      <td>33,314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category                           Sub-Category  \\\n",
       "0    Type of Household                         Married-couple   \n",
       "1    Type of Household  Female householder, no spouse present   \n",
       "2    Type of Household    Male householder, no spouse present   \n",
       "3    Type of Household                     Female householder   \n",
       "4    Type of Household                       Male householder   \n",
       "5   Race and Ethnicity                                  White   \n",
       "6   Race and Ethnicity                                  Black   \n",
       "7   Race and Ethnicity                                  Asian   \n",
       "8   Race and Ethnicity                    Hispanic (any race)   \n",
       "9   Age of Householder                         18 to 24 years   \n",
       "10  Age of Householder                         25 to 34 years   \n",
       "11  Age of Householder                         35 to 44 years   \n",
       "12  Age of Householder                         45 to 54 years   \n",
       "13  Age of Householder                         55 to 64 years   \n",
       "14  Age of Householder                     65 years and older   \n",
       "\n",
       "   Total Citizen Population (thousands)  \\\n",
       "0                               114,350   \n",
       "1                                 1,503   \n",
       "2                                 1,359   \n",
       "3                                30,089   \n",
       "4                                33,101   \n",
       "5                               177,865   \n",
       "6                                28,808   \n",
       "7                                10,283   \n",
       "8                                26,662   \n",
       "9                                26,913   \n",
       "10                               38,283   \n",
       "11                               34,327   \n",
       "12                               38,301   \n",
       "13                               39,242   \n",
       "14                               46,993   \n",
       "\n",
       "   Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "0                                  99,595                     79,382  \n",
       "1                                   1,230                        820  \n",
       "2                                   1,078                        701  \n",
       "3                                  24,561                     16,043  \n",
       "4                                  26,603                     14,855  \n",
       "5                                 152,285                    111,891  \n",
       "6                                  23,716                     17,119  \n",
       "7                                   8,252                      5,043  \n",
       "8                                  21,661                     12,682  \n",
       "9                                  21,555                     11,560  \n",
       "10                                 31,868                     20,332  \n",
       "11                                 29,228                     20,662  \n",
       "12                                 32,683                     25,012  \n",
       "13                                 33,774                     26,657  \n",
       "14                                 41,110                     33,314  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check 2016 Voter Data CSV to see if it reads properly & generate Data Frame\n",
    "csv_path5 = Path(\"2016_VotingCH_Data.csv\")\n",
    "voter3_df = pd.read_csv(csv_path5, encoding=\"UTF=8\")\n",
    "voter3_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "4fc8b2cf-bb0c-4565-a66c-5c1313120c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>114,350</td>\n",
       "      <td>99,595</td>\n",
       "      <td>79,382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>1,503</td>\n",
       "      <td>1,230</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>1,359</td>\n",
       "      <td>1,078</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>30,089</td>\n",
       "      <td>24,561</td>\n",
       "      <td>16,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>33,101</td>\n",
       "      <td>26,603</td>\n",
       "      <td>14,855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Total Citizen Population (thousands) Voting-Eligible Population (thousands)  \\\n",
       "0                              114,350                                 99,595   \n",
       "1                                1,503                                  1,230   \n",
       "2                                1,359                                  1,078   \n",
       "3                               30,089                                 24,561   \n",
       "4                               33,101                                 26,603   \n",
       "\n",
       "  Reported Voted (thousands)  \n",
       "0                     79,382  \n",
       "1                        820  \n",
       "2                        701  \n",
       "3                     16,043  \n",
       "4                     14,855  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# House Type Voter DataFrame 2016\n",
    "votertype_df = voter3_df.iloc[0:5]\n",
    "votertype_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "9f3efb10-696c-441e-866e-6779800308e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>177,865</td>\n",
       "      <td>152,285</td>\n",
       "      <td>111,891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>28,808</td>\n",
       "      <td>23,716</td>\n",
       "      <td>17,119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>10,283</td>\n",
       "      <td>8,252</td>\n",
       "      <td>5,043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>26,662</td>\n",
       "      <td>21,661</td>\n",
       "      <td>12,682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category         Sub-Category  \\\n",
       "5  Race and Ethnicity                White   \n",
       "6  Race and Ethnicity                Black   \n",
       "7  Race and Ethnicity                Asian   \n",
       "8  Race and Ethnicity  Hispanic (any race)   \n",
       "\n",
       "  Total Citizen Population (thousands) Voting-Eligible Population (thousands)  \\\n",
       "5                              177,865                                152,285   \n",
       "6                               28,808                                 23,716   \n",
       "7                               10,283                                  8,252   \n",
       "8                               26,662                                 21,661   \n",
       "\n",
       "  Reported Voted (thousands)  \n",
       "5                    111,891  \n",
       "6                     17,119  \n",
       "7                      5,043  \n",
       "8                     12,682  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voting Ethnicity DataFrame 2016\n",
    "voter_ethnicity = voter3_df.iloc[5:9]\n",
    "voter_ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "168dbe8c-88cc-4586-ab78-b7928b70a8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>18 to 24 years</td>\n",
       "      <td>26,913</td>\n",
       "      <td>21,555</td>\n",
       "      <td>11,560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>38,283</td>\n",
       "      <td>31,868</td>\n",
       "      <td>20,332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>34,327</td>\n",
       "      <td>29,228</td>\n",
       "      <td>20,662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>38,301</td>\n",
       "      <td>32,683</td>\n",
       "      <td>25,012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>39,242</td>\n",
       "      <td>33,774</td>\n",
       "      <td>26,657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>46,993</td>\n",
       "      <td>41,110</td>\n",
       "      <td>33,314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category        Sub-Category  \\\n",
       "9   Age of Householder      18 to 24 years   \n",
       "10  Age of Householder      25 to 34 years   \n",
       "11  Age of Householder      35 to 44 years   \n",
       "12  Age of Householder      45 to 54 years   \n",
       "13  Age of Householder      55 to 64 years   \n",
       "14  Age of Householder  65 years and older   \n",
       "\n",
       "   Total Citizen Population (thousands)  \\\n",
       "9                                26,913   \n",
       "10                               38,283   \n",
       "11                               34,327   \n",
       "12                               38,301   \n",
       "13                               39,242   \n",
       "14                               46,993   \n",
       "\n",
       "   Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "9                                  21,555                     11,560  \n",
       "10                                 31,868                     20,332  \n",
       "11                                 29,228                     20,662  \n",
       "12                                 32,683                     25,012  \n",
       "13                                 33,774                     26,657  \n",
       "14                                 41,110                     33,314  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voter Age DataFrame 2016\n",
    "voter_age = voter3_df.iloc[9:15]\n",
    "voter_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "3dad2bdf-f6d7-4392-85d5-7537067c8cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Reported Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>115,943</td>\n",
       "      <td>99,605</td>\n",
       "      <td>85,811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>1,528</td>\n",
       "      <td>1,212</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>1,234</td>\n",
       "      <td>960</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>32,467</td>\n",
       "      <td>26,550</td>\n",
       "      <td>19,651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>36,770</td>\n",
       "      <td>29,334</td>\n",
       "      <td>19,529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>181,891</td>\n",
       "      <td>154,437</td>\n",
       "      <td>124,301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>30,204</td>\n",
       "      <td>24,022</td>\n",
       "      <td>18,922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>11,530</td>\n",
       "      <td>8,989</td>\n",
       "      <td>6,881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>30,627</td>\n",
       "      <td>24,598</td>\n",
       "      <td>16,459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>18 to 24 years</td>\n",
       "      <td>26,737</td>\n",
       "      <td>21,405</td>\n",
       "      <td>13,752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>39,962</td>\n",
       "      <td>33,048</td>\n",
       "      <td>24,091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>35,977</td>\n",
       "      <td>30,129</td>\n",
       "      <td>23,421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>35,914</td>\n",
       "      <td>30,129</td>\n",
       "      <td>24,791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>39,676</td>\n",
       "      <td>33,821</td>\n",
       "      <td>28,855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>53,326</td>\n",
       "      <td>45,558</td>\n",
       "      <td>39,718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category                           Sub-Category  \\\n",
       "0    Type of Household                         Married-couple   \n",
       "1    Type of Household  Female householder, no spouse present   \n",
       "2    Type of Household    Male householder, no spouse present   \n",
       "3    Type of Household                     Female householder   \n",
       "4    Type of Household                       Male householder   \n",
       "5   Race and Ethnicity                                  White   \n",
       "6   Race and Ethnicity                                  Black   \n",
       "7   Race and Ethnicity                                  Asian   \n",
       "8   Race and Ethnicity                    Hispanic (any race)   \n",
       "9   Age of Householder                         18 to 24 years   \n",
       "10  Age of Householder                         25 to 34 years   \n",
       "11  Age of Householder                         35 to 44 years   \n",
       "12  Age of Householder                         45 to 54 years   \n",
       "13  Age of Householder                         55 to 64 years   \n",
       "14  Age of Householder                     65 years and older   \n",
       "\n",
       "   Total Citizen Population (thousands)  \\\n",
       "0                               115,943   \n",
       "1                                 1,528   \n",
       "2                                 1,234   \n",
       "3                                32,467   \n",
       "4                                36,770   \n",
       "5                               181,891   \n",
       "6                                30,204   \n",
       "7                                11,530   \n",
       "8                                30,627   \n",
       "9                                26,737   \n",
       "10                               39,962   \n",
       "11                               35,977   \n",
       "12                               35,914   \n",
       "13                               39,676   \n",
       "14                               53,326   \n",
       "\n",
       "   Reported Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "0                                           99,605                     85,811  \n",
       "1                                            1,212                        900  \n",
       "2                                              960                        641  \n",
       "3                                           26,550                     19,651  \n",
       "4                                           29,334                     19,529  \n",
       "5                                          154,437                    124,301  \n",
       "6                                           24,022                     18,922  \n",
       "7                                            8,989                      6,881  \n",
       "8                                           24,598                     16,459  \n",
       "9                                           21,405                     13,752  \n",
       "10                                          33,048                     24,091  \n",
       "11                                          30,129                     23,421  \n",
       "12                                          30,129                     24,791  \n",
       "13                                          33,821                     28,855  \n",
       "14                                          45,558                     39,718  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check 2020 Voter Data CSV to see if it reads properly & generate Data Frame\n",
    "csv_path6 = Path(\"2020_VotingCH_Data.csv\")\n",
    "voter4_df = pd.read_csv(csv_path6, encoding=\"UTF=8\")\n",
    "voter4_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "619d1237-380c-4043-88d1-bd7413e2f5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Reported Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Married-couple</td>\n",
       "      <td>115,943</td>\n",
       "      <td>99,605</td>\n",
       "      <td>85,811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder, no spouse present</td>\n",
       "      <td>1,528</td>\n",
       "      <td>1,212</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder, no spouse present</td>\n",
       "      <td>1,234</td>\n",
       "      <td>960</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Female householder</td>\n",
       "      <td>32,467</td>\n",
       "      <td>26,550</td>\n",
       "      <td>19,651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Type of Household</td>\n",
       "      <td>Male householder</td>\n",
       "      <td>36,770</td>\n",
       "      <td>29,334</td>\n",
       "      <td>19,529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category                           Sub-Category  \\\n",
       "0  Type of Household                         Married-couple   \n",
       "1  Type of Household  Female householder, no spouse present   \n",
       "2  Type of Household    Male householder, no spouse present   \n",
       "3  Type of Household                     Female householder   \n",
       "4  Type of Household                       Male householder   \n",
       "\n",
       "  Total Citizen Population (thousands)  \\\n",
       "0                              115,943   \n",
       "1                                1,528   \n",
       "2                                1,234   \n",
       "3                               32,467   \n",
       "4                               36,770   \n",
       "\n",
       "  Reported Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "0                                          99,605                     85,811  \n",
       "1                                           1,212                        900  \n",
       "2                                             960                        641  \n",
       "3                                          26,550                     19,651  \n",
       "4                                          29,334                     19,529  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# House Type Voter DataFrame 2020\n",
    "votertype2_df = voter4_df.iloc[0:5]\n",
    "votertype2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "a9068cb2-93d7-4268-adf5-1cfea59af290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Reported Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>181,891</td>\n",
       "      <td>154,437</td>\n",
       "      <td>124,301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>30,204</td>\n",
       "      <td>24,022</td>\n",
       "      <td>18,922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Asian</td>\n",
       "      <td>11,530</td>\n",
       "      <td>8,989</td>\n",
       "      <td>6,881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Race and Ethnicity</td>\n",
       "      <td>Hispanic (any race)</td>\n",
       "      <td>30,627</td>\n",
       "      <td>24,598</td>\n",
       "      <td>16,459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category         Sub-Category  \\\n",
       "5  Race and Ethnicity                White   \n",
       "6  Race and Ethnicity                Black   \n",
       "7  Race and Ethnicity                Asian   \n",
       "8  Race and Ethnicity  Hispanic (any race)   \n",
       "\n",
       "  Total Citizen Population (thousands)  \\\n",
       "5                              181,891   \n",
       "6                               30,204   \n",
       "7                               11,530   \n",
       "8                               30,627   \n",
       "\n",
       "  Reported Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "5                                         154,437                    124,301  \n",
       "6                                          24,022                     18,922  \n",
       "7                                           8,989                      6,881  \n",
       "8                                          24,598                     16,459  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voting Ethnicity DataFrame 2016\n",
    "voter_ethnicity2 = voter4_df.iloc[5:9]\n",
    "voter_ethnicity2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "2acdd6b0-3d84-4679-b0e9-2340dc74b604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Total Citizen Population (thousands)</th>\n",
       "      <th>Reported Voting-Eligible Population (thousands)</th>\n",
       "      <th>Reported Voted (thousands)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>18 to 24 years</td>\n",
       "      <td>26,737</td>\n",
       "      <td>21,405</td>\n",
       "      <td>13,752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>25 to 34 years</td>\n",
       "      <td>39,962</td>\n",
       "      <td>33,048</td>\n",
       "      <td>24,091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>35 to 44 years</td>\n",
       "      <td>35,977</td>\n",
       "      <td>30,129</td>\n",
       "      <td>23,421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>45 to 54 years</td>\n",
       "      <td>35,914</td>\n",
       "      <td>30,129</td>\n",
       "      <td>24,791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>55 to 64 years</td>\n",
       "      <td>39,676</td>\n",
       "      <td>33,821</td>\n",
       "      <td>28,855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Age of Householder</td>\n",
       "      <td>65 years and older</td>\n",
       "      <td>53,326</td>\n",
       "      <td>45,558</td>\n",
       "      <td>39,718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category        Sub-Category  \\\n",
       "9   Age of Householder      18 to 24 years   \n",
       "10  Age of Householder      25 to 34 years   \n",
       "11  Age of Householder      35 to 44 years   \n",
       "12  Age of Householder      45 to 54 years   \n",
       "13  Age of Householder      55 to 64 years   \n",
       "14  Age of Householder  65 years and older   \n",
       "\n",
       "   Total Citizen Population (thousands)  \\\n",
       "9                                26,737   \n",
       "10                               39,962   \n",
       "11                               35,977   \n",
       "12                               35,914   \n",
       "13                               39,676   \n",
       "14                               53,326   \n",
       "\n",
       "   Reported Voting-Eligible Population (thousands) Reported Voted (thousands)  \n",
       "9                                           21,405                     13,752  \n",
       "10                                          33,048                     24,091  \n",
       "11                                          30,129                     23,421  \n",
       "12                                          30,129                     24,791  \n",
       "13                                          33,821                     28,855  \n",
       "14                                          45,558                     39,718  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Voter Age DataFrame 2016\n",
    "voter_age2 = voter4_df.iloc[9:15]\n",
    "voter_age2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "fc1a422f-1dbb-4190-9650-457515a1665a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Household type\n",
    "household_type = [\"Married-Couple\", \"Female householder, no spouse present\", \"Male householder, no spouse present\", \"Female householder\", \"Male householder\"]\n",
    "\n",
    "# Data for 2016 and 2020\n",
    "median_income_2016 = [87057, 41027, 58051, 30572, 41749]\n",
    "median_income_2020 = [101517, 49214, 67304, 35574, 47259]\n",
    "reported_votes_2016 = [79382, 820, 701, 16043, 14855]\n",
    "reported_votes_2020 = [85811, 900, 641, 19651, 19529]\n",
    "\n",
    "# Create scatter plot\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Plot 2016 data\n",
    "plt.scatter(reported_votes_2016, median_income_2016, color='orange', label='2016 Data', s=100)\n",
    "\n",
    "# Annotate each point with the household type\n",
    "for i, household in enumerate(household_type):\n",
    "    plt.text(reported_votes_2016[i], median_income_2016[i], household, fontsize=9, ha='left')\n",
    "    \n",
    "\n",
    "# Plot 2020 data\n",
    "plt.scatter(reported_votes_2020, median_income_2020, color='green', label='2020 Data', s=100)\n",
    "\n",
    "# Annotate each point with the household type\n",
    "for i, household in enumerate(household_type):\n",
    "    plt.text(reported_votes_2020[i], median_income_2020[i], household, fontsize=9, ha='left')\n",
    "\n",
    "# Add labels, title, and legend\n",
    "plt.xlabel('Reported Votes (Thousands)')\n",
    "plt.ylabel('Median Income (Dollars)')\n",
    "plt.title('Household Type vs Median Income & Voting Reported')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('HouseType&Voting.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "a8faa175-ea2e-451f-9beb-fa9ce36b5640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Race and Ethnicity \n",
    "race_ethnicity = [\"White\", \"Black\", \"Asian\", \"Hispanic (any race)\"]\n",
    "\n",
    "# Data for 2016 and 2020\n",
    "median_income_2016 = [61858, 39490, 81431, 47675]\n",
    "median_income_2020 = [71231, 45870, 94903, 55321]\n",
    "reported_votes_2016 = [111891, 17119, 5043, 12682]\n",
    "reported_votes_2020 = [124301, 18922, 6881, 16459]\n",
    "\n",
    "# Create scatter plot\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Plot 2016 data\n",
    "plt.scatter(reported_votes_2016, median_income_2016, color='orange', label='2016 Data', s=100)\n",
    "\n",
    "# Annotate each point with race and ethnicity\n",
    "for i, ethnicity in enumerate(race_ethnicity):\n",
    "    plt.text(reported_votes_2016[i], median_income_2016[i], ethnicity, fontsize=9, ha='left')\n",
    "    \n",
    "\n",
    "# Plot 2020 data\n",
    "plt.scatter(reported_votes_2020, median_income_2020, color='green', label='2020 Data', s=100)\n",
    "\n",
    "# Annotate each point with the race and ethnicity\n",
    "for i, ethnicity in enumerate(race_ethnicity):\n",
    "    plt.text(reported_votes_2020[i], median_income_2020[i], ethnicity, fontsize=9, ha='left')\n",
    "\n",
    "# Add labels, title, and legend\n",
    "plt.xlabel('Reported Votes (Thousands)')\n",
    "plt.ylabel('Median Income (Dollars)')\n",
    "plt.title('Race and Ethnicity vs Median Income & Voting Reported')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Ethnicity&Voting.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "b8fd0936-0d68-4234-b46f-6fb540cb456f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age ranges\n",
    "age_ranges = [\"18 to 24 years\", \"25 to 34 years\", \"35 to 44 years\", \"45 to 54 years\", \"55 to 64 years\", \"65 years and older\"]\n",
    "\n",
    "# Data for 2016 and 2020\n",
    "median_income_2016 = [41655, 60932, 74481, 77213, 65239, 39823]\n",
    "median_income_2020 = [46886, 71566, 85694, 90359, 74270, 46360]\n",
    "reported_votes_2016 = [11560, 20332, 20662, 25012, 26657, 33314]\n",
    "reported_votes_2020 = [13752, 24091, 23421, 24791, 28855, 39718]\n",
    "\n",
    "# Create scatter plot\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Plot 2016 data\n",
    "plt.scatter(reported_votes_2016, median_income_2016, color='orange', label='2016 Data', s=100)\n",
    "\n",
    "# Annotate each point with the age range\n",
    "for i, age in enumerate(age_ranges):\n",
    "    plt.text(reported_votes_2016[i], median_income_2016[i], age, fontsize=9, ha='left')\n",
    "    \n",
    "\n",
    "# Plot 2020 data\n",
    "plt.scatter(reported_votes_2020, median_income_2020, color='green', label='2020 Data', s=100)\n",
    "\n",
    "# Annotate each point with the age range\n",
    "for i, age in enumerate(age_ranges):\n",
    "    plt.text(reported_votes_2020[i], median_income_2020[i], age, fontsize=9, ha='left')\n",
    "\n",
    "# Add labels, title, and legend\n",
    "plt.xlabel('Reported Votes (Thousands)')\n",
    "plt.ylabel('Median Income (Dollars)')\n",
    "plt.title('Age vs Median Income & Voting Reported')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('Age&Voting.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
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